Surfaces in range image understanding
Surfaces in range image understanding
Induction of one-level decision trees
ML92 Proceedings of the ninth international workshop on Machine learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognizing Action at a Distance
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Background Modeling and Subtraction of Dynamic Scenes
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Improved Adaptive Gaussian Mixture Model for Background Subtraction
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 2 - Volume 02
Recognizing Human Actions: A Local SVM Approach
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Actions Sketch: A Novel Action Representation
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Spatiograms versus Histograms for Region-Based Tracking
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
International Journal of Computer Vision
View Invariance for Human Action Recognition
International Journal of Computer Vision
Behavior recognition via sparse spatio-temporal features
ICCCN '05 Proceedings of the 14th International Conference on Computer Communications and Networks
Moving object segmentation by background subtraction and temporal analysis
Image and Vision Computing
Human activity analysis: A review
ACM Computing Surveys (CSUR)
Real-time human pose recognition in parts from single depth images
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Learning to efficiently detect repeatable interest points in depth data
ECCV'12 Proceedings of the 12th European conference on Computer Vision - Volume Part I
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We present a new method to classify human activities by leveraging on the cues available from depth images alone. Towards this end, we propose a descriptor which couples depth and spatial information of the segmented body to describe a human pose. Unique poses (i.e. codewords) are then identified by a spatial-based clustering step. Given a video sequence of depth images, we segment humans from the depth images and represent these segmented bodies as a sequence of codewords. We exploit unique poses of an activity and the temporal ordering of these poses to learn subsequences of codewords which are strongly discriminative for the activity. Each discriminative subsequence acts as a classifier and we learn a boosted ensemble of discriminative subsequences to assign a confidence score for the activity label of the test sequence. Unlike existing methods which demand accurate tracking of 3D joint locations or couple depth with color image information as recognition cues, our method requires only the segmentation masks from depth images to recognize an activity. Experimental results on the publicly available Human Activity Dataset (which comprises 12 challenging activities) demonstrate the validity of our method, where we attain a precision/recall of 78.1%/75.4% when the person was not seen before in the training set, and 94.6%/93.1% when the person was seen before.